M.J. Reinders (Marcel)http://repub.eur.nl/ppl/1913/
List of Publicationsenhttp://repub.eur.nl/eur_signature.pnghttp://repub.eur.nl/
RePub, Erasmus University RepositoryTwo splice-factor mutant leukemia subgroups uncovered at the boundaries of MDS and AML using combined gene expression and DNA-methylation profilinghttp://repub.eur.nl/pub/54214/
Thu, 22 May 2014 00:00:01 GMT<div>E. Taskesen</div><div>M. Havermans</div><div>K. van Lom</div><div>M.A. Sanders</div><div>Y. van Norden</div><div>E.M.J. Bindels</div><div>R. Hoogenboezem</div><div>M.J. Reinders</div><div>M.E. Figueroa</div><div>P.J.M. Valk</div><div>B. Löwenberg</div><div>A. Melnick</div><div>H.R. Delwel</div>
Mutations in splice factor (SF) genes occurmore frequently inmyelodysplastic syndromes (MDS) than in acute myeloid leukemias (AML). We sequenced complementary DNA from bonemarrowof 47 refractory anemiawithexcess blasts (RAEB) patients, 29AML cases with low marrow blast cell count, and 325 other AML patients and determined the presence of SFhotspot mutations in SF3B1, U2AF35, and SRSF2. SFmutations were found in 10 RAEB, 12 AML cases with low marrow blast cell count, and 25 other AML cases. Our study provides evidence that SF-mutant RAEB and SF-mutant AML are clinically, cytologically, and molecularly highly similar. An integrated analysis of genomewidemessenger RNA (mRNA) expression profiling and DNA-methylation profiling data revealed 2 unique patient clusters highly enriched for SF-mutant RAEB/AML. The combined genomewide mRNA expression profiling/DNA-methylation profiling signatures revealed 1 SF-mutant patient clusterwith an erythroid signature. The other SF-mutant patient cluster was enriched for NRAS/KRAS mutations and showed an inferior survival.We conclude that SF-mutant RAEB/AML constitutes a related disorder overriding the artificial separation between AML and MDS, and that SF-mutant RAEB/AML is composed of 2 molecularly and clinically distinct subgroups.We conclude that SF-mutant disorders should be considered as myeloid malignancies that transcend the boundaries of AML and MDS.Hypergeometric analysis of tiling-array and sequence data: Detection and interpretation of peakshttp://repub.eur.nl/pub/74790/
Thu, 05 Dec 2013 00:00:01 GMT<div>E. Taskesen</div><div>R. Hoogeboezem</div><div>H.R. Delwel</div><div>M.J. Reinders</div>
Probing protein-deoxyribonucleic acid (DNA) is gaining popularity as it sheds light on molecular mechanisms that regulate the expression of genes. Currently, tiling-arrays and next-generation sequencing technology can be used to measure these interactions. Both methods generate a signal over the genome in which contiguous regions of peaks on the genome represent the presence of an interacting molecule. Many methods do exist to identify functional regions of interest (ROIs) on the genome. However the detection of ROIs are often not an end-point in research questions and it therefore requires data dragging between tools to relate the ROIs to information present in databases, such as gene-ontology, pathway information, or enrichment of certain genomic content. We introduce hypergeometric analysis of tiling-array and sequence data (HATSEQ), a powerful tool that accurately identifies functional ROIs on the genome where a genomic signal significantly deviates from the general genome-wide behavior. HATSEQ also includes a number of built-in post-analyses with which biological meaning can be attached to the detected ROIs in terms of gene pathways and de-novo motif analysis, and provides different visualizations and statistical summaries for the detected ROIs. In addition, HATSEQ has an intuitive graphic user interface that lowers the barrier for researchers to analyze their data without the need of scripting languages. We compared the results of HATSEQ against two other popular chromatin immunoprecipitation sequencing (ChIP-Seq) methods and observed overlap in the detected ROIs but HATSEQ is more specific in delineating the peak boundaries. We also discuss the versatility of HATSEQ by using a Signal Transducer and Activator of Transcription 1 (STAT1) ChIP-Seq data-set, and show that the detected ROIs are highly specific for the expected STAT1 binding motif. HATSEQ is freely available at: http://hema13.erasmusmc.nl/index.php/HATSEQ.High content imaging in the screening of biomaterial-induced MSC behaviorhttp://repub.eur.nl/pub/70153/
Fri, 01 Feb 2013 00:00:01 GMT<div>H.V. Unadkat</div><div>N. Groen</div><div>J. Doorn</div><div>B. Fischer</div><div>A.M.C. Barradas</div><div>M. Hulsman</div><div>J. van de Peppel</div><div>L. Moroni</div><div>J.P.T.M. van Leeuwen</div><div>M.J. Reinders</div><div>C.A. van Blitterswijk</div><div>J. de Boer</div>
Upon contact with a biomaterial, cells and surrounding tissues respond in a manner dictated by the physicochemical and mechanical properties of the material. Traditionally, cellular responses are monitored using invasive analytical methods that report the expression of genes or proteins. These analytical methods involve assessing commonly used markers for a predefined readout, masking the actual situation induced in the cells. Hence, a broader expression profile of the cellular response should be envisioned, which technically limits up scaling to higher throughput systems. However, it is increasingly recognized that morphometric readouts, obtained non-invasively, are related to gene expression patterns. Here, we introduced distinct surface roughness to three PLA surfaces, by exposure to oxygen plasma of different duration times. The response of mesenchymal stromal cells was compared to smooth untreated PLA surfaces without the addition of differentiation agents. Morphological and genome wide expression profiles revealed underlying cellular changes which was hidden for the commonly used gene markers for osteo-, chondro- and adipogenesis. Using 3 morphometric parameters, obtained by high content imaging, we were able to build a classifier and discriminate between oxygen plasma-induced modified sheets and non-modified PLA sheets where evaluating classical candidates missed this effect. This approach shows the feasibility to use noninvasive morphometric data in high-throughput systems to screen biomaterial surfaces indicating the underlying genetic biomaterial-induced changes.The livestock sector and its stakeholders in the search to meet the animal welfare requirements of societyhttp://repub.eur.nl/pub/71835/
Tue, 01 Jan 2013 00:00:01 GMT<div>V.M. Immink</div><div>M.J. Reinders</div><div>R.J.M. van Tulder</div><div>J.C.M. van Trijp</div>
Gene therapy: Is IL2RG oncogenic in T-cell development?http://repub.eur.nl/pub/53876/
Thu, 21 Sep 2006 00:00:01 GMT<div>K. Pike</div><div>G. Wagemaker</div><div>J.J.M. van Dongen</div><div>F.J.T. Staal</div><div>D. de Ridder</div><div>F. Weerkamp</div><div>M.R. Baert</div><div>M.M.A. Verstegen</div><div>M.H. Brugman</div><div>S.J. Howe</div><div>M.J. Reinders</div><div>A.J. Thrasher</div>
The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies.http://repub.eur.nl/pub/13984/
Thu, 02 Mar 2006 00:00:01 GMT<div>R.G.W. Verhaak</div><div>F.J.T. Staal</div><div>P.J.M. Valk</div><div>B. Löwenberg</div><div>M.J. Reinders</div><div>D. de Ridder</div>
BACKGROUND: Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers. RESULTS: Using two publicly available datasets, i.e., gene-expression data of 285 patients with Acute Myeloid Leukemia (AML, Affymetrix HG-U133A GeneChip) and 42 samples of tumor tissue of the embryonal central nervous system (CNS, Affymetrix HuGeneFL GeneChip), we tested the effect of the four pre-processing strategies mentioned above, on (1) expression level measurements, (2) detection of differential expression, (3) cluster analysis and (4) classification of samples. In most cases, the effect of pre-processing is relatively small compared to other choices made in an analysis for the AML dataset, but has a more profound effect on the outcome of the CNS dataset. Analyses on individual probe sets, such as testing for differential expression, are affected most; supervised, multivariate analyses such as classification are far less sensitive to pre-processing. CONCLUSION: Using two experimental datasets, we show that the choice of pre-processing method is of relatively minor influence on the final analysis outcome of large microarray studies whereas it can have important effects on the results of a smaller study. The data source (platform, tissue homogeneity, RNA quality) is potentially of bigger importance than the choice of pre-processing method.Maximum significance clustering of oligonucleotide microarrayshttp://repub.eur.nl/pub/57872/
Wed, 01 Feb 2006 00:00:01 GMT<div>D. de Ridder</div><div>F.J.T. Staal</div><div>J.J.M. van Dongen</div><div>M.J. Reinders</div>
Motivation: Affymetrix high-density oligonucleotide microarrays measure the expression of DNA transcripts using probesets, i.e. multiple probes per transcript. Usually, these multiple measurements are transformed into a single probeset expression level before data analysis proceeds; any information on variability is lost. In this paper we demonstrate how individual probe measurements can be used in a statistic for differential expression. Furthermore, we show how this statistic can serve as a criterion for clustering microarrays. Results: A novel clust ering algorithm using this maximum significance criterion is demonstrated to be more efficient with the measured data than competing techniques for dealing with repeated measurements, especially when the sample size is small.New insights on human T cell development by quantitative T cell receptor gene rearrangement studies and gene expression profilinghttp://repub.eur.nl/pub/61742/
Mon, 06 Jun 2005 00:00:01 GMT<div>W.A. Dik</div><div>J.J.M. van Dongen</div><div>A.W. Langerak</div><div>F.J.T. Staal</div><div>K. Pike</div><div>F. Weerkamp</div><div>D. de Ridder</div><div>E.F. de Haas</div><div>M.R. Baert</div><div>P.J. van der Spek</div><div>E.E. Koster</div><div>M.J. Reinders</div>
To gain more insight into initiation and regulation of T cell receptor (TCR) gene rearrangement during human T cell development, we analyzed TCR gene rearrangements by quantitative PCR analysis in nine consecutive T cell developmental stages, including CD34+ lin- cord blood cells as a reference. The same stages were used for gene expression profiling using DNA microarrays. We show that TCR loci rearrange in a highly ordered way (TCRD-TCRG-TCRB-TCRA) and that the initiating Dδ2-Dδ3 rearrangement occurs at the most immature CD34+CD38-CD1a- stage. TCRB rearrangement starts at the CD34+CD38 +CD1a- stage and complete in-frame TCRB rearrangements were first detected in the immature single positive stage. TCRB rearrangement data together with the PTCRA (pTα) expression pattern show that human TCRβ-selection occurs at the CD34+CD38+CD1a + stage. By combining the TCR rearrangement data with gene expression data, we identified candidate factors for the initiation/regulation of TCR recombination. Our data demonstrate that a number of key events occur earlier than assumed previously; therefore, human T cell development is much more similar to murine T cell development than reported before. JEMPurity for clarity: The need for purification of tumor cells in DNA microarray studieshttp://repub.eur.nl/pub/70946/
Fri, 01 Apr 2005 00:00:01 GMT<div>D. de Ridder</div><div>C.E. van der Linden</div><div>T. Schonewille</div><div>W.A. Dik</div><div>M.J. Reinders</div><div>J.J.M. van Dongen</div><div>F.J.T. Staal</div>
It is now well established that gene expression profiling using DNA microarrays can provide novel information about various types of hematological malignancies, which may lead to identification of novel diagnostic markers. However, to successfully use microarrays for this purpose, the quality and reproducibility of the procedure need to be guaranteed. The quality of microarray analyses may be severely reduced, if variable frequencies of nontarget cells are present in the starting material. To systematically investigate the influence of different types of impurity, we determined gene expression profiles of leukemic samples containing different percentages of nonleukemic leukocytes. Furthermore, we used computer simulations to study the affect of different kinds of impurity as an alternative to conducting hundreds of microarray experiments on samples with various levels of purity. As expected, the percentage of erroneously identified genes rose with the increase of contaminating nontarget cells in the samples. The simulations demonstrated that a tumor load of less than 75% can lead to up to 25% erroneously identified genes. A tumor load of at least 90% leads to identification of at most 5% false-positive genes. We therefore propose that in order to draw well-founded conclusions, the percentage of target cells in microarray experiment samples should be at least 90%.New insights on human T cell development by quantitative T cell receptor gene rearrangement studies and gene expression profilinghttp://repub.eur.nl/pub/8406/
Sat, 01 Jan 2005 00:00:01 GMT<div>W.A. Dik</div><div>J.J.M. van Dongen</div><div>F.J.T. Staal</div><div>A.W. Langerak</div><div>F. Weerkamp</div><div>D. de Ridder</div><div>E.F. de Haas</div><div>M.R. Baert</div><div>E.E. Koster</div><div>M.J. Reinders</div><div>P.J. van der Spek</div><div>K. Pike</div>
To gain more insight into initiation and regulation of T cell receptor
(TCR) gene rearrangement during human T cell development, we analyzed TCR
gene rearrangements by quantitative PCR analysis in nine consecutive T
cell developmental stages, including CD34+ lin- cord blood cells as a
reference. The same stages were used for gene expression profiling using
DNA microarrays. We show that TCR loci rearrange in a highly ordered way
(TCRD-TCRG-TCRB-TCRA) and that the initiating Ddelta2-Ddelta3
rearrangement occurs at the most immature CD34+CD38-CD1a- stage. TCRB
rearrangement starts at the CD34+CD38+CD1a- stage and complete in-frame
TCRB rearrangements were first detected in the immature single positive
stage. TCRB rearrangement data together with the PTCRA (pTalpha)
expression pattern show that human TCRbeta-selection occurs at the
CD34+CD38+CD1a+ stage. By combining the TCR rearrangement data with gene
expression data, we identified candidate factors for the
initiation/regulation of TCR recombination. Our data demonstrate that a
number of key events occur earlier than assumed previously; therefore,
human T cell development is much more similar to murine T cell development
than reported before.DNA microarrays for comparison of gene expression profiles between diagnosis and relapse in precursor-B acute lymphoblastic leukemia: Choice of technique and purification influence the identification of potential diagnostic markershttp://repub.eur.nl/pub/66241/
Tue, 01 Jul 2003 00:00:01 GMT<div>F.J.T. Staal</div><div>M. van der Burg</div><div>L. Wessels</div><div>B.H. Barendregt</div><div>M.R. Baert</div><div>C.M. van den Burg</div><div>C. van Huffel</div><div>A.W. Langerak</div><div>V.H.J. van der Velden</div><div>M.J. Reinders</div><div>J.J.M. van Dongen</div>